{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Python36_64\\Lib\\site-packages\\numpy\\_distributor_init.py:32: UserWarning: loaded more than 1 DLL from .libs:\n",
      "E:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Python36_64\\Lib\\site-packages\\numpy\\.libs\\libopenblas.PYQHXLVVQ7VESDPUVUADXEVJOBGHJPAY.gfortran-win_amd64.dll\n",
      "E:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Python36_64\\Lib\\site-packages\\numpy\\.libs\\libopenblas.TXA6YQSD3GCQQC22GEQ54J2UDCXDXHWN.gfortran-win_amd64.dll\n",
      "  stacklevel=1)\n",
      "E:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Python36_64\\Lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "E:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Python36_64\\Lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "E:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Python36_64\\Lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "E:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Python36_64\\Lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "E:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Python36_64\\Lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:521: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "E:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Python36_64\\Lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#!/usr/bin/python \n",
    "\n",
    "# -*- coding: utf-8 -*-\n",
    "\n",
    "import pandas as pd\n",
    "import lightgbm as lgb\n",
    "import xgboost as xgb\n",
    "from sklearn.linear_model import BayesianRidge\n",
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import OneHotEncoder, LabelEncoder\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as mp\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import seaborn as sns\n",
    "import random\n",
    "#from untitled0 import MeanEncoder\n",
    " \n",
    "import tensorflow as tf\n",
    "import random as rn\n",
    " \n",
    "#random seeds for stochastic parts of neural network \n",
    "np.random.seed(10)\n",
    "from tensorflow import set_random_seed\n",
    "set_random_seed(15)\n",
    " \n",
    "from keras.models import Model\n",
    "from keras.layers import Input, Dense, Concatenate, Reshape, Dropout\n",
    "from keras.layers.embeddings import Embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('F:/dc_xiamen/train.csv')\n",
    "train_target = pd.read_csv('F:/dc_xiamen/train_target.csv')\n",
    "train = train.merge(train_target,on='id')\n",
    "test = pd.read_csv('F:/dc_xiamen/test.csv')\n",
    "\n",
    "#观测到在5到6.5左右，训练集的lmt出现了测试集中没有的峰值波动，于是考虑到正负样本的比例，在这里扔掉一部分负样本中对应的lmt部分，使lmt分布保持一致\n",
    "train1 = train.loc[train.target==0]\n",
    "train2 = train.loc[train.target==1]\n",
    "a = train1['lmt'].between(5,5.2)\n",
    "train1 = train1[~a]\n",
    "a = train1['lmt'].between(6,6.2)\n",
    "train1 = train1[~a] \n",
    "\n",
    "train = pd.concat([train1,train2],axis=0,ignore_index=True)\n",
    "\n",
    "test['target'] = -999\n",
    "data = pd.concat([train,test],axis=0,ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#特征工程\n",
    "#0. 补全缺失值\n",
    "data = data.fillna(-999).dropna()\n",
    "\n",
    "#1.对x相关特征处理,x为征信字段，假设征信字段意义类似，可以利用统计的方法实现部分特征提取\n",
    "x_feat = ['x_{}'.format(i) for i in range(79)]\n",
    "    \n",
    "data['x_count'] = data[x_feat].sum(axis=1)\n",
    "data['x_var'] = data[x_feat].var(axis=1)\n",
    "\n",
    "#2.isNew相关  这里假设isnew=0的数据为两个月前的数据（微信群提到），通过组合特征提取相关特征\n",
    "for i in ['certId']:\n",
    "    a = data.loc[data.isNew==1]\n",
    "    b = data.loc[data.isNew==0]\n",
    "    e = a.groupby([i])['id'].count().reset_index(name='1_count')\n",
    "    data = data.merge(e,on='certId',how='left')\n",
    "    \n",
    "    t = b.groupby([i])['id'].count().reset_index(name='0_count')\n",
    "    data = data.merge(t,on='certId',how='left')\n",
    "\n",
    "#3. 构造计数和排序特征\n",
    "count_fea = ['certId','dist','job','lmt','bankCard','residentAddr','setupHour','weekday','ethnic']\n",
    "for i in count_fea:\n",
    "    data[i+'_count'] = data.groupby(i)['id'].transform('count')\n",
    "    data[i+'_rank'] = data.groupby(i)['id'].transform('rank')\n",
    "\n",
    "#4， 缺失值统计，统计存在缺失值的特征，构造缺失值相关计数特征\n",
    "loss_fea = ['bankCard','residentAddr','highestEdu','linkRela']\n",
    "for i in loss_fea:\n",
    "    a = data.loc[data[i]==-999]\n",
    "    e = a.groupby(['certId'])['id'].count().reset_index(name=i+'_certId_count') \n",
    "    data = data.merge(e,on='certId',how='left')\n",
    "    \n",
    "    d = a.groupby(['loanProduct'])['id'].count().reset_index(name=i+'_loan_count') \n",
    "    data = data.merge(d,on='loanProduct',how='left')\n",
    "    \n",
    "    m = a.groupby(['job'])['id'].count().reset_index(name=i+'_job_count') \n",
    "    data = data.merge(m,on='job',how='left')\n",
    "    \n",
    "    data['certloss_'+i] = data[i+'_certId_count']/data['certId_count']\n",
    "    data['jobloss_'+i] = data[i+'_job_count']/data['job_count']\n",
    "\n",
    "#5.时间戳处理  将时间段进行转化并处理\n",
    "data['begintime'] =pd.to_datetime(data['certValidBegin']*1000000000)-pd.offsets.DateOffset(years=70)\n",
    "data['today'] =pd.to_datetime('2019-10-29')\n",
    "data['day'] =(data['today'] - data['begintime']).map(lambda x:x.days)\n",
    "data['time_range'] = data['certValidStop']-data['certValidBegin']    #证件时间段\n",
    "\n",
    "del data['today']\n",
    "del data['certValidBegin']\n",
    "del data['certValidStop']\n",
    "del data['begintime']\n",
    "del data['5yearBadloan']\n",
    "del data['job_rank']\n",
    "\n",
    "#6.lmt相关 观察线上线下得知lmt是重要特征，于是进行额外处理\n",
    "for i in ['certId','bankCard']:\n",
    "#    data['lmt_mean'+i] = data.groupby(i)['lmt'].transform('count')\n",
    "    data['lmt_nunique'+i] = data.groupby(i)['lmt'].transform('nunique')\n",
    "\n",
    "#7.bankcard相关 特征预处理得知bankcard，dist，certId，residengAddr在意义上存在强相关性，于是根据实际意义进行处理\n",
    "\n",
    "data['bankcard_null'] = data['bankCard'].map(lambda x:1 if x==-999  else 0)     #bankcard是否缺失\n",
    "#data['minority'] = data['ethnic'].map(lambda x:1 if x>0 else 0)     #是否是少数民族\n",
    "data['distance'] = abs(data['certId']-data['dist'])    #贷款地与原户籍地差\n",
    "data['distance2'] = abs(data['certId']-data['residentAddr']) \n",
    "#data['weekend'] = data['weekday'].map(lambda x:1 if x>5 else 0)  #是否周末\n",
    "\n",
    "#8. 根据eda扔掉表现不好的特征\n",
    "del data['unpayIndvLoan']\n",
    "del data['unpayOtherLoan']\n",
    "del data['unpayNormalLoan']\n",
    "del data['setupHour_rank']\n",
    "del data['residentAddr_job_count']\n",
    "del data['linkRela_job_count']\n",
    "\n",
    "x_feat = ['x_{}'.format(i) for i in [66,74,47,63,52,54,51,26,25,73,65,30,12,61,76,44,49,69,35]]\n",
    "for i in x_feat:\n",
    "    del data[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 0\n",
      "Training until validation scores don't improve for 3000 rounds.\n",
      "[1000]\ttraining's auc: 0.781304\tvalid_1's auc: 0.69221\n",
      "[2000]\ttraining's auc: 0.814298\tvalid_1's auc: 0.702427\n",
      "[3000]\ttraining's auc: 0.838299\tvalid_1's auc: 0.706619\n",
      "[4000]\ttraining's auc: 0.857365\tvalid_1's auc: 0.706577\n",
      "[5000]\ttraining's auc: 0.872831\tvalid_1's auc: 0.707151\n",
      "[6000]\ttraining's auc: 0.885559\tvalid_1's auc: 0.705889\n",
      "[7000]\ttraining's auc: 0.896274\tvalid_1's auc: 0.703841\n",
      "Early stopping, best iteration is:\n",
      "[4798]\ttraining's auc: 0.869987\tvalid_1's auc: 0.707311\n",
      "Fold 1\n",
      "Training until validation scores don't improve for 3000 rounds.\n",
      "[1000]\ttraining's auc: 0.778231\tvalid_1's auc: 0.692301\n",
      "[2000]\ttraining's auc: 0.810355\tvalid_1's auc: 0.703283\n",
      "[3000]\ttraining's auc: 0.835443\tvalid_1's auc: 0.710649\n",
      "[4000]\ttraining's auc: 0.853823\tvalid_1's auc: 0.71444\n",
      "[5000]\ttraining's auc: 0.869267\tvalid_1's auc: 0.715246\n",
      "[6000]\ttraining's auc: 0.882183\tvalid_1's auc: 0.71527\n",
      "[7000]\ttraining's auc: 0.893191\tvalid_1's auc: 0.715728\n",
      "[8000]\ttraining's auc: 0.90277\tvalid_1's auc: 0.715438\n",
      "[9000]\ttraining's auc: 0.910579\tvalid_1's auc: 0.71296\n",
      "Early stopping, best iteration is:\n",
      "[6955]\ttraining's auc: 0.892741\tvalid_1's auc: 0.716032\n",
      "Fold 2\n",
      "Training until validation scores don't improve for 3000 rounds.\n",
      "[1000]\ttraining's auc: 0.779891\tvalid_1's auc: 0.698293\n",
      "[2000]\ttraining's auc: 0.811973\tvalid_1's auc: 0.712888\n",
      "[3000]\ttraining's auc: 0.834036\tvalid_1's auc: 0.71474\n",
      "[4000]\ttraining's auc: 0.851908\tvalid_1's auc: 0.711373\n",
      "[5000]\ttraining's auc: 0.867122\tvalid_1's auc: 0.71279\n",
      "Early stopping, best iteration is:\n",
      "[2620]\ttraining's auc: 0.826515\tvalid_1's auc: 0.716488\n",
      "Fold 3\n",
      "Training until validation scores don't improve for 3000 rounds.\n",
      "[1000]\ttraining's auc: 0.778742\tvalid_1's auc: 0.759364\n",
      "[2000]\ttraining's auc: 0.810795\tvalid_1's auc: 0.752189\n",
      "[3000]\ttraining's auc: 0.834693\tvalid_1's auc: 0.750753\n",
      "Early stopping, best iteration is:\n",
      "[282]\ttraining's auc: 0.740622\tvalid_1's auc: 0.76657\n",
      "Fold 4\n",
      "Training until validation scores don't improve for 3000 rounds.\n",
      "[1000]\ttraining's auc: 0.777285\tvalid_1's auc: 0.7539\n",
      "[2000]\ttraining's auc: 0.810528\tvalid_1's auc: 0.755217\n",
      "[3000]\ttraining's auc: 0.834543\tvalid_1's auc: 0.756837\n",
      "Early stopping, best iteration is:\n",
      "[122]\ttraining's auc: 0.723141\tvalid_1's auc: 0.764149\n",
      "Fold 5\n",
      "Training until validation scores don't improve for 3000 rounds.\n",
      "[1000]\ttraining's auc: 0.779027\tvalid_1's auc: 0.708497\n",
      "[2000]\ttraining's auc: 0.811989\tvalid_1's auc: 0.709089\n",
      "[3000]\ttraining's auc: 0.837919\tvalid_1's auc: 0.70823\n",
      "[4000]\ttraining's auc: 0.857148\tvalid_1's auc: 0.703072\n",
      "[5000]\ttraining's auc: 0.872077\tvalid_1's auc: 0.698693\n",
      "Early stopping, best iteration is:\n",
      "[2326]\ttraining's auc: 0.821186\tvalid_1's auc: 0.710207\n",
      "Fold 6\n",
      "Training until validation scores don't improve for 3000 rounds.\n",
      "[1000]\ttraining's auc: 0.782215\tvalid_1's auc: 0.690007\n",
      "[2000]\ttraining's auc: 0.812817\tvalid_1's auc: 0.684442\n",
      "[3000]\ttraining's auc: 0.837372\tvalid_1's auc: 0.679685\n",
      "Early stopping, best iteration is:\n",
      "[261]\ttraining's auc: 0.745004\tvalid_1's auc: 0.691542\n",
      "Fold 7\n",
      "Training until validation scores don't improve for 3000 rounds.\n",
      "[1000]\ttraining's auc: 0.780736\tvalid_1's auc: 0.67902\n",
      "[2000]\ttraining's auc: 0.814811\tvalid_1's auc: 0.677747\n",
      "[3000]\ttraining's auc: 0.839405\tvalid_1's auc: 0.680083\n",
      "[4000]\ttraining's auc: 0.857189\tvalid_1's auc: 0.676497\n",
      "[5000]\ttraining's auc: 0.871455\tvalid_1's auc: 0.670391\n",
      "Early stopping, best iteration is:\n",
      "[2954]\ttraining's auc: 0.838424\tvalid_1's auc: 0.680774\n",
      "Fold 8\n",
      "Training until validation scores don't improve for 3000 rounds.\n",
      "[1000]\ttraining's auc: 0.77697\tvalid_1's auc: 0.695159\n",
      "[2000]\ttraining's auc: 0.812067\tvalid_1's auc: 0.700785\n",
      "[3000]\ttraining's auc: 0.835922\tvalid_1's auc: 0.700284\n",
      "[4000]\ttraining's auc: 0.854105\tvalid_1's auc: 0.704009\n",
      "[5000]\ttraining's auc: 0.868458\tvalid_1's auc: 0.704261\n",
      "[6000]\ttraining's auc: 0.881334\tvalid_1's auc: 0.703867\n",
      "[7000]\ttraining's auc: 0.892539\tvalid_1's auc: 0.702177\n",
      "Early stopping, best iteration is:\n",
      "[4547]\ttraining's auc: 0.862228\tvalid_1's auc: 0.704815\n",
      "Fold 9\n",
      "Training until validation scores don't improve for 3000 rounds.\n",
      "[1000]\ttraining's auc: 0.779722\tvalid_1's auc: 0.718891\n",
      "[2000]\ttraining's auc: 0.812043\tvalid_1's auc: 0.713743\n",
      "[3000]\ttraining's auc: 0.835836\tvalid_1's auc: 0.712051\n",
      "[4000]\ttraining's auc: 0.854982\tvalid_1's auc: 0.706302\n",
      "Early stopping, best iteration is:\n",
      "[1075]\ttraining's auc: 0.782934\tvalid_1's auc: 0.719595\n",
      "CV score: 0.70742 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x2016 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#lgb\n",
    "train_df = data[data['target']>=0]\n",
    "test = data[data['target']<0]\n",
    "\n",
    "\n",
    "features = [c for c in data.columns if c not in ['target','id']]\n",
    "target = train_df['target']\n",
    "\n",
    "param = {\n",
    "    'boosting_type': 'gbdt',\n",
    "    'objective': 'binary',\n",
    "    'metric': 'auc',\n",
    "    'learning_rate': 0.01,\n",
    "    'num_leaves': 2 ** 5 - 1,\n",
    "    'min_child_samples':50,\n",
    "    'max_bin':50,\n",
    "    'min_child_weight': 0,\n",
    "    'scale_pos_weight': 15,\n",
    "    'feature_fraction_seed':2019 ,\n",
    "    'max_depth':2,\n",
    "    'nthread': 4,\n",
    "    'verbose': 0,\n",
    "    'lambda_l1': 1,\n",
    "    'lambda_l2': 0\n",
    "               }\n",
    "folds = StratifiedKFold(n_splits=10, shuffle=False, random_state=2020)\n",
    "oof = np.zeros(len(train_df))\n",
    "predictions = np.zeros(len(test))\n",
    "feature_importance_df = pd.DataFrame()\n",
    "\n",
    "for fold_, (trn_idx, val_idx) in enumerate(folds.split(train_df.values, target.values)):\n",
    "    print(\"Fold {}\".format(fold_))\n",
    "    trn_data = lgb.Dataset(train_df.iloc[trn_idx][features], label=target.iloc[trn_idx])\n",
    "    val_data = lgb.Dataset(train_df.iloc[val_idx][features], label=target.iloc[val_idx])\n",
    "\n",
    "    num_round = 1000000\n",
    "    clf = lgb.train(param, trn_data, num_round, valid_sets = [trn_data, val_data], verbose_eval=1000, early_stopping_rounds = 3000)\n",
    "    oof[val_idx] = clf.predict(train_df.iloc[val_idx][features], num_iteration=clf.best_iteration)\n",
    "    \n",
    "    fold_importance_df = pd.DataFrame()\n",
    "    fold_importance_df[\"Feature\"] = features\n",
    "    fold_importance_df[\"importance\"] = clf.feature_importance()\n",
    "    fold_importance_df[\"fold\"] = fold_ + 1\n",
    "    feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0)\n",
    "    \n",
    "    predictions += clf.predict(test[features], num_iteration=clf.best_iteration) / folds.n_splits\n",
    "\n",
    "print(\"CV score: {:<8.5f}\".format(roc_auc_score(target, oof)))\n",
    "\n",
    "#画图 \n",
    "cols = (feature_importance_df[[\"Feature\", \"importance\"]]\n",
    "        .groupby(\"Feature\")\n",
    "        .mean()\n",
    "        .sort_values(by=\"importance\", ascending=False)[:150].index)\n",
    "best_features = feature_importance_df.loc[feature_importance_df.Feature.isin(cols)]\n",
    "\n",
    "plt.figure(figsize=(14,28))\n",
    "sns.barplot(x=\"importance\", y=\"Feature\", data=best_features.sort_values(by=\"importance\",ascending=False))\n",
    "plt.title('Features importance (averaged/folds)')\n",
    "plt.tight_layout()\n",
    "plt.savefig('F:/26.png')\n",
    "\n",
    "sub_df = pd.DataFrame({\"id\":test[\"id\"].values})\n",
    "sub_df[\"target\"] = predictions.round(6)\n",
    "sub_df.to_csv(\"F:/submission27.csv\", index=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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